Color Treatment and Color Normalization for Digital Assets

ABSTRACT

Devices, methods, and non-transitory program storage devices are disclosed to provide for the application of color treatments and/or color normalization operations to digital assets (DAs) in a set of DAs that are to be displayed as part of a multimedia presentation. The determination of the color treatment may be based on comparing one or more characteristics of an audio media item associated with the set of DAs to a corresponding one or more characteristics of a plurality of predetermined color treatments. Color normalization may be applied to the set of DAs prior to the determined color treatment. Techniques disclosed herein may also determine one or more parameters for a multimedia presentation of the set of DAs based on a characteristic of the associated audio media item. The parameters for the multimedia presentation may comprise one or more of: preferred DA sequences, portions, clusters, layouts, themes, transition types, or transition durations.

TECHNICAL FIELD

This disclosure relates generally to the field of image processing. Moreparticularly, but not by way of limitation, it relates to techniques forapplying color normalization, selected color treatments, and/ortransitions to a set of digital assets (e.g., images, videos, etc.) tobe included in a multimedia presentation based, at least in part, on acharacteristic of an audio media item (e.g., a song, soundtrack, etc.)associated with the set of digital assets.

BACKGROUND

Modern consumer electronics have enabled users to create, purchase, andamass considerable amounts of digital assets, or “DAs.” For example, acomputing system (e.g., a smartphone, a stationary computer system, aportable computer system, a media player, a tablet computer system, awearable computer system or device, etc.) can store or have access to acollection of digital assets (also referred to as a DA collection) thatincludes hundreds or thousands of DAs.

Managing a DA collection can be a resource-intensive exercise for users.For example, retrieving multiple DAs representing an important moment orevent in a user's life from a sizable DA collection can require the userto sift through many irrelevant DAs. This process can be arduous andunpleasant for many users. A digital asset management (DAM) system canassist with managing a DA collection. A DAM system represents anintertwined system incorporating software, hardware, and/or otherservices in order to manage, store, ingest, organize, retrieve, andpresent DAs in a DA collection. An important building block for at leastone commonly available DAM system is a database. Databases comprise datacollections that are organized as schemas, tables, queries, reports,views, and other objects. Exemplary databases include relationaldatabases (e.g., tabular databases, etc.), distributed databases thatcan be dispersed or replicated among different points in a network, andobject-oriented programming databases that can be congruent with thedata defined in object classes and subclasses.

However, one problem associated with using databases for digital assetmanagement is that the DAM system can become resource-intensive tostore, manage, and update. That is, substantial computational resourcesmay be needed to manage the DAs in the DA collection (e.g., processingpower for performing queries or transactions, storage memory space forstoring the necessary databases, etc.). Another related problemassociated with using databases is that DAM cannot easily be implementedon a computing system with limited storage capacity without managing theassets directly (e.g., a portable or personal computing system, such asa smartphone or a wearable device). Consequently, a DAM system'sfunctionality is generally provided by a remote device (e.g., anexternal data store, an external server, etc.), where copies of the DAsare stored, and the results are transmitted back to the computing systemhaving limited storage capacity.

Thus, according to some DAM embodiments, a DAM may further comprise aknowledge graph metadata network (also referred to herein as simply a“knowledge graph” or “metadata network”) associated with a collection ofdigital assets (i.e., a DA collection). The metadata network cancomprise correlated metadata assets describing characteristicsassociated with digital assets in the DA collection. Each metadata assetcan describe a characteristic associated with one or more digital assets(DAs) in the DA collection. For example, a metadata asset can describe acharacteristic associated with multiple DAs in the DA collection, suchas the location, day of week, event type, etc., of the one or moreassociated DAs. Each metadata asset can be represented as a node in themetadata network. A metadata asset can be correlated with at least oneother metadata asset. Each correlation between metadata assets can berepresented as an edge in the metadata network that is between the nodesrepresenting the correlated metadata assets. According to someembodiments, the metadata networks may define multiple types of nodesand edges, e.g., each with their own properties, based on the needs of agiven implementation.

In addition to the aforementioned difficulties that a user may face inmanaging a large DA collection (e.g., locating and/or retrievingmultiple DAs representing an important moment, event, person, location,theme, or topic in a user's life), users may also struggle to determine(or be unable to spend the time it would take to determine) which DAswould be meaningful to view (e.g., in the form of a multimediapresentation, such as a slideshow) and/or share with third parties,e.g., other users of similar DAM systems and/or social contacts of theuser. Users may also not want to spend the time it would take todetermine suitable DA sequences, clusters, layouts, themes, transitiontypes and durations, edits to the duration or content of individual DAs,etc., for constructing such a multimedia presentation of a user's DAs.Further, users may struggle to determine a suitable audio media item(e.g., one or more songs to be used as a “soundtrack”) to associate withthe playback of a set of DAs that is to be included in a multimediapresentation. Finally, users may struggle to determine (or not even becognizant of) what types of color treatments (e.g., color normalizationand/or color grading operations) would help enhance the look and feel ofthe set of DAs to be displayed in the multimedia presentation.

Thus, there is a need for methods, apparatuses, computer readable media,and systems to provide users with more intelligent and automated DAcolor treatment suggestions (and/or other content-related suggestions)for multimedia presentations of a set of DAs, e.g., based on a selectedaudio media item associated with the set of DAs and/or one or moreproperties of the DAs in the set of DAs.

SUMMARY

Devices, methods, and non-transitory program storage devices (NPSDs) aredisclosed herein to provide for the application of color treatmentsand/or color normalization operations to DAs in a set of DAs that are tobe displayed as part of a multimedia presentation. In some embodiments,the determination of the color treatment may be based on comparing oneor more characteristics of an audio media item associated with the setof DAs to a corresponding one or more characteristics of a plurality ofpredetermined color treatments. In some such embodiments, colornormalization may be applied to the set of DAs prior to the applicationof the determined color treatment. The techniques disclosed herein mayalso determine one or more parameters for a multimedia presentation ofthe set of DAs based on a characteristic of an associated audio mediaitem. In some embodiments, the determined parameters for the multimediapresentation may comprise one or more of: preferred DA sequences,clusters, layouts, themes, transition types or transition durations,edits to the duration or content of individual DAs, etc.

Thus, according to some embodiments, there is provided a device,comprising: a memory; a display; and one or more processors operativelycoupled to the memory, wherein the one or more processors are configuredto execute instructions causing the one or more processors to: obtain afirst set of digital assets; obtain a first audio media item associatedwith the first set of digital assets, wherein the first audio media itemcomprises at least a first audio characteristic metadata item; determinea first color treatment, wherein the determination of the first colortreatment is based, at least in part, on the first audio characteristicmetadata item of the first audio media item; and apply the determinedfirst color treatment to at least one of the digital assets of the firstset of digital assets.

According to other embodiments, there is provided a device, comprising:a memory; a display; and one or more processors operatively coupled tothe memory, wherein the one or more processors are configured to executeinstructions causing the one or more processors to: obtain a first setof digital assets; obtain a first audio media item associated with thefirst set of digital assets, wherein the first audio media itemcomprises at least a first audio characteristic metadata item; anddetermine one or more parameters for a multimedia presentation of thefirst set of digital assets based, at least in part, on the first audiocharacteristic metadata item of the first audio media item.

According to still other embodiments, there is provided a device,comprising: a memory; a display; and one or more processors operativelycoupled to the memory, wherein the one or more processors are configuredto execute instructions causing the one or more processors to: obtain afirst digital image; apply the first digital image to a first deepneural network (DNN), wherein the first DNN has been trained to learn afirst target image style, wherein the first DNN is configured todetermine a first set of image parameter modifications for a first setof image parameters of the first digital image, and wherein applicationof the determined first set of image parameter modifications to thefirst digital image would cause the first digital image to approximatethe first target style; and apply the determined first set of imageparameter modifications to the first digital image.

Various non-transitory program storage device (NPSD) embodiments arealso disclosed herein. Such NPSDs are readable by one or moreprocessors. Instructions may be stored on the NPSDs for causing the oneor more processors to perform any of the embodiments disclosed herein.Various image processing methods are also disclosed herein, inaccordance with the device and NPSD embodiments disclosed herein.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates, in block diagram form, an asset managementprocessing system that includes electronic components for performingdigital asset management (DAM), according to one or more embodiments.

FIG. 2 illustrates an exemplary user interface for selecting a colortreatment to be applied to a set of DAs to be displayed as part of amultimedia presentation with an associated audio media item, accordingto one or more embodiments.

FIG. 3 illustrates a color treatment determination process based on acharacteristic of a selected audio media item for a multimediapresentation of a set of DAs, according to one or more embodiments.

FIG. 4A illustrates examples of possible digital asset layouts for theDAs to be displayed in a multimedia presentation, according to one ormore embodiments.

FIG. 4B illustrates examples of possible digital asset clusters,transition types, and transition durations for the DAs to be displayedin a multimedia presentation, according to one or more embodiments.

FIG. 5 illustrates an exemplary deep neural network (DNN) architecturefor a network trained to learn image parameter modifications to apply toan image to approximate a target style, according to one or moreembodiments.

FIG. 6A is a flow chart illustrating a method of determining a colortreatment for a set of DAs based on a characteristic of an associatedaudio media item, according to various embodiments.

FIG. 6B is a flow chart illustrating a method of determining one or moreparameters for a multimedia presentation of a set of DAs based on acharacteristic of an associated audio media item, according to variousembodiments.

FIG. 6C is a flow chart illustrating a method of using a DNN trained tolearn image parameter modifications to apply to an image to approximatea target style, according to various embodiments.

FIG. 7 is a block diagram illustrating a programmable electroniccomputing device, in which one or more of the techniques disclosedherein may be implemented.

DETAILED DESCRIPTION

In the following description, for purposes of explanation, numerousspecific details are set forth in order to provide a thoroughunderstanding of the inventions disclosed herein. It will be apparent,however, to one skilled in the art that the inventions may be practicedwithout these specific details. In other instances, structure anddevices are shown in block diagram form in order to avoid obscuring theinventions. References to numbers without subscripts or suffixes areunderstood to reference all instance of subscripts and suffixescorresponding to the referenced number. Moreover, the language used inthis disclosure has been principally selected for readability andinstructional purposes and may not have been selected to delineate orcircumscribe the inventive subject matter, and, thus, resort to theclaims may be necessary to determine such inventive subject matter.Reference in the specification to “one embodiment” or to “an embodiment”(or similar) means that a particular feature, structure, orcharacteristic described in connection with the embodiments is includedin at least one embodiment of one of the inventions, and multiplereferences to “one embodiment” or “an embodiment” should not beunderstood as necessarily all referring to the same embodiment.

Embodiments set forth herein can assist with improving computerfunctionality by enabling computing systems that use one or moreembodiments of the digital asset management (DAM) systems describedherein. Such computing systems can implement DAM to assist with reducingor eliminating the need for users to manually determine what DAs toinclude in multimedia presentations and how such DAs should bepresented, e.g., in terms of multimedia presentation parameters, such asDA layout, clustering, sequencing, transitioning, and selection of anappropriate associated audio media item (e.g., a “soundtrack” for themultimedia presentation)—as well as in terms of selecting a colortreatment for the DAs that will complement the content of the DAs, theparameters of the multimedia presentation, and/or the audiocharacteristics of the selected audio media item.

This reduction or elimination can, in turn, assist with minimizingwasted computational resources (e.g., memory, processing power,computational time, etc.) that may be associated with using exclusivelyrelational databases for DAM. For example, performing DAM via relationaldatabases may include external data stores and/or remote servers (aswell as networks, communication protocols, and other components requiredfor communicating with external data stores and/or remote servers). Incontrast, DAM performed as described herein (i.e., leveraging aknowledge graph metadata network) can occur locally on a device (e.g., aportable computing system, a wearable computing system, etc.) withoutthe need for external data stores, remote servers, networks,communication protocols, and/or other components required forcommunicating with external data stores and/or remote servers.

Moreover, by automating the process of suggesting color treatmentsand/or presentation parameters for multimedia presentationsautomatically generated based on information stored in a knowledge graphmetadata network, users do not have to perform as much manualexamination of their (often quite large) DA collections to determinewhat DAs might be appropriate to share together as part of multimediapresentation and/or how such DAs might need to be color corrected, colormodified, and/or sequenced for inclusion in such a multimediapresentation based, e.g., on a characteristic of an audio media itemassociated with the multimedia presentation. Consequently, at least oneembodiment of DAM described herein can assist with reducing oreliminating the additional computational resources (e.g., memory,processing power, computational time, etc.) that may be associated witha user's searching, sorting, sequencing, and/or color modifying DAsobtained manually from external relational databases in order todetermine whether or not to include such DAs in multimedia presentationsto be displayed to the user and/or shared with one or more thirdparties.

Exemplary Client Device for Digital Asset Management and MultimediaPresentation-Related Information Storage

Turning now to FIG. 1 , an asset management processing system, i.e.,client device system 100, is illustrated that includes electroniccomponents for performing digital asset management (DAM), according toone or more embodiments. The system 100 can be housed in singlecomputing system, such as a desktop computer system, a laptop computersystem, a tablet computer system, a server computer system, a mobilephone, a media player, a personal digital assistant (PDA), a personalcommunicator, a gaming device, a network router or hub, a wirelessaccess point (AP) or repeater, a set-top box, or a combination thereof.Components in the system 100 can be spatially separated and implementedon separate computing systems that are operatively connected, asdescribed in further detail below.

For one embodiment, the system 100 may include processing unit(s) 104,memory 110, a DA capture device(s) 102, sensor(s) 122, and peripheral(s)118. For one embodiment, one or more components in the system 100 may beimplemented as one or more integrated circuits (ICs). For example, atleast one of the processing unit(s) 104, the DA capture device 102, theperipheral(s) 118, the sensor(s) 122, or the memory 110 can beimplemented as a system-on-a-chip (SoC) IC, a three-dimensional (3D) IC,any other known IC, or any known IC combination. For another embodiment,two or more components in the system 100 are implemented together as oneor more ICs. For example, at least two of the processing unit(s) 104,the DA capture device 102, the peripheral(s) 118, the sensor(s) 122, orthe memory 110 are implemented together as an SoC IC. Each component ofsystem 100 is described below.

As shown in FIG. 1 , the system 100 can include processing unit(s) 104,such as CPUs, GPUs, other integrated circuits (ICs), memory, and/orother electronic circuitry. For one embodiment, the processing unit(s)104 manipulate and/or process DA metadata associated with digital assets112 or multimedia presentation data 116 associated with digital assets(e.g., preferred DA sequences, clusters, layouts, themes, transitiontypes and durations, edits to the duration or content of individual DAs,etc.). The processing unit(s) 104 may include a digital asset management(DAM) system 106 for performing one or more embodiments of DAM, asdescribed herein. For one embodiment, the DAM system 106 is implementedas hardware (e.g., electronic circuitry associated with the processingunit(s) 104, circuitry, dedicated logic, etc.), software (e.g., one ormore instructions associated with a computer program executed by theprocessing unit(s) 104, software run on a general-purpose computersystem or a dedicated machine, etc.), or a combination thereof.

The DAM system 106 can enable the system 100 to generate and use aknowledge graph metadata network (also referred to herein more simply as“knowledge graph” or “metadata network”) 108 of the DA metadata 112 as amultidimensional network. Metadata networks and multidimensionalnetworks that may be used to implement the various techniques describedherein are described in further detail in, e.g., U.S. Non-Provisionalpatent application Ser. No. 15/391,269, entitled “Notable Moments in aCollection of Digital Assets,” filed Dec. 27, 2016 (“the '269application”).

In one embodiment, the DAM system 106 can perform one or more of thefollowing operations: (i) generate the metadata network 108; (ii) relateand/or present at least two DAs, e.g., as part of a moment or multimediapresentation, based on the metadata network 108; (iii) determine and/orpresent interesting DAs (or sets of DAs) in the DA collection to theuser as viewing or sharing suggestions, based on the metadata network108 and one or more other criterion; and (iv) select and/or presentsuggested sets of DAs (along with, optionally, color treatment optionsand/or audio media item soundtrack options for such sets of DAs) forinclusion into a multimedia presentation to be displayed to a userand/or to be shared with one or more third parties, e.g., based on acontextual analysis of the DAs included in the multimedia presentation.Additional details about the immediately preceding operations that maybe performed by the DAM system 106 are described below and,particularly, in connection with FIGS. 6A-6C.

The DAM system 106 can obtain or receive a collection of DA metadata 112associated with a DA collection. As used herein, a “digital asset,” a“DA,” and their variations refer to data that can be stored in or as adigital form (e.g., a digital file etc.). This digitalized dataincludes, but is not limited to, the following: image media (e.g., astill or animated image, etc.); audio media (e.g., a song, etc.); textmedia (e.g., an E-book, etc.); video media (e.g., a movie, etc.); andhaptic media (e.g., vibrations or motions provided in connection withother media, etc.). The examples of digitalized data above can becombined to form multimedia (e.g., a computer animated cartoon, a videogame, a presentation, etc.). A single DA refers to a single instance ofdigitalized data (e.g., an image, a song, a movie, etc.). Multiple DAsor a group of DAs refers to multiple instances of digitalized data(e.g., multiple images, multiple songs, multiple movies, etc.).Throughout this disclosure, the use of “a DA” refers to “one or moreDAs” including a single DA and a group of DAs. For brevity, the conceptsset forth in this document use an operative example of a DA as one ormore images. It is to be appreciated that a DA is not so limited, andthe concepts set forth in this document are applicable to other DAs(e.g., the different media described above, etc.).

As used herein, a “digital asset collection,” a “DA collection,” andtheir variations refer to multiple DAs that may be stored in one or morestorage locations. The one or more storage locations may be spatially orlogically separated as is known.

As used herein, “metadata,” “digital asset metadata,” “DA metadata,” andtheir variations collectively refer to information about one or moreDAs. Metadata can be: (i) a single instance of information aboutdigitalized data (e.g., a time stamp associated with one or more images,etc.); or (ii) a grouping of metadata, which refers to a group comprisedof multiple instances of information about digitalized data (e.g.,several time stamps associated with one or more images, etc.). There mayalso be many different types of metadata associated with a collection ofDAs. Each type of metadata (also referred to as “metadata type”)describes one or more characteristics or attributes associated with oneor more DAs. Further detail regarding the various types of metadata thatmay be stored in a DA collection and/or utilized in conjunction with aknowledge graph metadata network are described in further detail in,e.g., the '269 application, which was incorporated by reference above.

As used herein, “context” and its variations refer to any or allattributes of a user's device that includes or has access to a DAcollection associated with the user, such as physical, logical, social,and other contextual information. As used herein, “contextualinformation” and its variations refer to metadata that describes ordefines a user's context or a context of a user's device that includesor has access to a DA collection associated with the user. Exemplarycontextual information includes, but is not limited to, the following: apredetermined time interval; an event scheduled to occur in apredetermined time interval; a geolocation visited during a particulartime interval; one or more identified persons associated with aparticular time interval; an event taking place during a particular timeinterval, or a geolocation visited during a particular time interval;weather metadata describing weather associated with a particular periodin time (e.g., rain, snow, sun, temperature, etc.); season metadatadescribing a season associated with the capture of one or more DAs;relationship information describing the nature of the socialrelationship between a user and one or more third parties; or naturallanguage processing (NLP) information describing the nature and/orcontent of an interaction between a user and one more third parties. Forsome embodiments, the contextual information can be obtained fromexternal sources, e.g., a social networking application, a weatherapplication, a calendar application, an address book application, anyother type of application, or from any type of data store accessible viaa wired or wireless network (e.g., the Internet, a private intranet,etc.).

Referring again to FIG. 1 , for one embodiment, the DAM system 106 usesthe DA metadata 112 to generate a metadata network 108. As shown in FIG.1 , all or some of the metadata network 108 can be stored in theprocessing unit(s) 104 and/or the memory 110. As used herein, a“knowledge graph,” a “knowledge graph metadata network,” a “metadatanetwork,” and their variations refer to a dynamically organizedcollection of metadata describing one or more DAs (e.g., one or moregroups of DAs in a DA collection, one or more DAs in a DA collection,etc.) used by one or more computer systems. In a metadata network, thereare no actual DAs stored—only metadata (e.g., metadata associated withone or more groups of DAs, metadata associated with one or more DAs,etc.). Metadata networks differ from databases because, in general, ametadata network enables deep connections between metadata usingmultiple dimensions, which can be traversed for additionally deducedcorrelations. This deductive reasoning generally is not feasible in aconventional relational database without loading a significant number ofdatabase tables (e.g., hundreds, thousands, etc.). As such, as alludedto above, conventional databases may require a large amount ofcomputational resources (e.g., external data stores, remote servers, andtheir associated communication technologies, etc.) to perform deductivereasoning. In contrast, a metadata network may be viewed, operated,and/or stored using fewer computational resource requirements than theconventional databases described above. Furthermore, metadata networksare dynamic resources that have the capacity to learn, grow, and adaptas new information is added to them. This is unlike databases, which areuseful for accessing cross-referred information. While a database can beexpanded with additional information, the database remains an instrumentfor accessing the cross-referred information that was put into it.Metadata networks do more than access cross-referenced information—theygo beyond that and involve the extrapolation of data for inferring ordetermining additional data. As alluded to above, the DAs themselves maybe stored, e.g., on one or more servers remote to the system 100, withthumbnail versions of the DAs stored in system memory 110 and fullversions of particular DAs only downloaded and/or stored to the system100's memory 110 as needed (e.g., when the user desires to view or sharea particular DA). In other embodiments, however, e.g., when the amountof onboard storage space and processing resources at the system 100 issufficiently large and/or the size of the user's DA collection issufficiently small, the DAs themselves may also be stored within memory110, e.g., in a separate database, such as the aforementionedconventional databases.

The DAM system 106 may generate the metadata network 108 as amultidimensional network of the DA metadata 112. As used herein, a“multidimensional network” and its variations refer to a complex graphhaving multiple kinds of relationships. A multidimensional networkgenerally includes multiple nodes and edges. For one embodiment, thenodes represent metadata, and the edges represent relationships orcorrelations between the metadata. Exemplary multidimensional networksinclude, but are not limited to, edge-labeled multigraphs, multipartiteedge-labeled multigraphs, and multilayer networks.

In one embodiment, the metadata network 108 includes two types ofnodes—(i) moment nodes; and (ii) non-moments nodes. As used herein,“moment” shall refer to a contextual organizational schema used to groupone or more digital assets, e.g., for the purpose of displaying thegroup of digital assets to a user, according to inferred orexplicitly-defined relatedness between such digital assets. For example,a moment may refer to a visit to coffee shop in Cupertino, Calif. thattook place on Mar. 26, 2018. In this example, the moment can be used toidentify one or more DAs (e.g., one image, a group of images, a video, agroup of videos, a song, a group of songs, etc.) associated with thevisit to the coffee shop on Mar. 26, 2018 (and not with any othermoment).

As used herein, a “moment node” refers to a node in a multidimensionalnetwork that represents a moment (as is described above). As usedherein, a “non-moment node” refers a node in a multidimensional networkthat does not represent a moment. Thus, a non-moment node may refer to ametadata asset associated with one or more DAs that is not a moment,e.g., a node associated with a particular person, location, ormultimedia presentation. Further details regarding the possible types of“non-moment” nodes that may be found in an exemplary metadata networkmay be found e.g., the '269 application, which was incorporated byreference above.

For one embodiment, the edges in the metadata network 108 between nodesrepresent relationships or correlations between the nodes. For oneembodiment, the DAM system 106 updates the metadata network 108 as itobtains or receives new metadata 112 and/or determines new metadata 112for the DAs in the user's DA collection.

The DAM system 106 can manage DAs associated with the DA metadata 112using the metadata network 108 in various ways. For a first example, DAMsystem 106 may use the metadata network 108 to identify and presentinteresting sets of one or more DAs in a DA collection based on thecorrelations (i.e., the edges in the metadata network 108) between theDA metadata (i.e., the nodes in the metadata network 108) and one ormore criterion. For this first example, the DAM system 106 may selectthe interesting DAs based on moment nodes in the metadata network 108.In some embodiments, the DAM system 106 may suggest that a user viewsand/or shares the one or more identified DAs with one or more thirdparties. For a second example, the DAM system 106 may use the metadatanetwork 108 and other contextual information gathered from the system(e.g., the user's relationship to one or more third parties, a topic ortypes of scene identified in the DAs related to one or moments, etc.) toselect and present a representative multimedia presentation includingone or more DAs that the user may want to view and/or share with one ormore third parties.

In some embodiments, the DAM system 106 can use a color treatmentdetermination module 105 to determine one or more suggested colortreatments for a given set of one or more DAs to be included in aparticular multimedia presentation, as described below and,particularly, in connection with FIGS. 2-3 . In some other embodiments,the DAM system 106 can use a color normalization module 107 to determineone or more suggested color normalization image modification operationto apply to a given set of one or more DAs to be included in aparticular multimedia presentation, e.g., prior to the application of adetermined color treatment, as described below and, particularly, inconnection with FIG. 5 . In still other embodiments, the DAM system 106can use a multimedia presentation determination module 109 to determineone or more parameters for a particular multimedia presentation, asdescribed below and, particularly, in connection with FIGS. 4A-4B.

The system 100 can also include memory 110 for storing and/or retrievingmetadata 112, the metadata network 108, and/or multimedia presentationdata 116 described by or associated with the metadata 112. The metadata112, the metadata network 108, and/or the multimedia presentation data116 can be generated, processed, and/or captured by the other componentsin the system 100. For example, the metadata 112, the metadata network108, and/or the multimedia presentation data 116 may include datagenerated by, captured by, processed by, or associated with one or moreperipherals 118, the DA capture device(s) 102, or the processing unit(s)104, etc. The system 100 can also include a memory controller (notshown), which includes at least one electronic circuit that manages dataflowing to and/or from the memory 110. The memory controller can be aseparate processing unit or integrated in processing unit(s) 104.

The system 100 can include a DA capture device(s) 102 (e.g., an imagingdevice for capturing images, an audio device for capturing sounds, amultimedia device for capturing audio and video, any other known DAcapture device, etc.). Device 102 is illustrated with a dashed box toshow that it is an optional component of the system 100. For oneembodiment, the DA capture device 102 can also include a signalprocessing pipeline that is implemented as hardware, software, or acombination thereof. The signal processing pipeline can perform one ormore operations on data received from one or more components in thedevice 102. The signal processing pipeline can also provide processeddata to the memory 110, the peripheral(s) 118 (as discussed furtherbelow), and/or the processing unit(s) 104.

The system 100 can also include peripheral(s) 118. For one embodiment,the peripheral(s) 118 can include at least one of the following: (i) oneor more input devices that interact with or send data to one or morecomponents in the system 100 (e.g., mouse, keyboards, etc.); (ii) one ormore output devices that provide output from one or more components inthe system 100 (e.g., monitors, printers, display devices, etc.); or(iii) one or more storage devices that store data in addition to thememory 110. Peripheral(s) 118 is illustrated with a dashed box to showthat it is an optional component of the system 100. The peripheral(s)118 may also refer to a single component or device that can be used bothas an input and output device (e.g., a touch screen, etc.). The system100 may include at least one peripheral control circuit (not shown) forthe peripheral(s) 118. The peripheral control circuit can be acontroller (e.g., a chip, an expansion card, or a stand-alone device,etc.) that interfaces with and is used to direct operation(s) performedby the peripheral(s) 118. The peripheral(s) controller can be a separateprocessing unit or integrated in processing unit(s) 104. Theperipheral(s) 118 can also be referred to as input/output (I/O) devices118 throughout this document.

The system 100 can also include one or more sensors 122, which areillustrated with a dashed box to show that the sensor can be optionalcomponents of the system 100. For one embodiment, the sensor(s) 122 candetect a characteristic of one or more environs. Examples of a sensorinclude, but are not limited to: a light sensor, an imaging sensor, anaccelerometer, a sound sensor, a barometric sensor, a proximity sensor,a vibration sensor, a gyroscopic sensor, a compass, a barometer, a heatsensor, a rotation sensor, a velocity sensor, and an inclinometer.

For one or more embodiments, the system 100 also includes communicationmechanism 120. The communication mechanism 120 can be, e.g., a bus, anetwork, or a switch. When the technology 120 is a bus, the technology120 is a communication system that transfers data between components insystem 100, or between components in system 100 and other componentsassociated with other systems (not shown). As a bus, the technology 120includes all related hardware components (wire, optical fiber, etc.)and/or software, including communication protocols. For one embodiment,the technology 120 can include an internal bus and/or an external bus.Moreover, the technology 120 can include a control bus, an address bus,and/or a data bus for communications associated with the system 100. Forone embodiment, the technology 120 can be a network or a switch. As anetwork, the technology 120 may be any network such as a local areanetwork (LAN), a wide area network (WAN) such as the Internet, a fibernetwork, a storage network, or a combination thereof, wired or wireless.When the technology 120 is a network, the components in the system 100do not have to be physically co-located. Separate components in system100 may be linked directly over a network even though these componentsmay not be physically located next to each other. For example, two ormore of the processing unit(s) 104, the communication technology 120,the memory 110, the peripheral(s) 118, the sensor(s) 122, and the DAcapture device(s) 102 may be in distinct physical locations from eachother and be communicatively coupled via the communication technology120, which may be a network or a switch that directly links thesecomponents over a network.

Determining Color Treatments for Sets of Digital Assets Based onCharacteristics of Associated Audio Media Items

Turning now to FIG. 2 , an exemplary user interface 200 for selecting acolor treatment to be applied to a set of DAs to be displayed as part ofa multimedia presentation with an associated audio media item isillustrated, according to one or more embodiments. As described above,DAM systems as described herein are capable of generating multimediapresentations (also referred to herein as “Memories” or “movies”), aswell as parameters describing such multimedia presentations. In otherembodiments, it is to be understood that a user may manually select theDAs to be included in a generated multimedia presentation and/ormanually adjust the automatic suggestions of a DAM system of DAs to beincluded in a generated multimedia presentation. In some embodiments,such multimedia presentations may also be associated with one or moreaudio media items, e.g., one or more songs to be used as a “soundtrack”with the playback of a set of DAs that is to be included in a multimediapresentation. In some such embodiments, it may also be desirable for theDAM to determine and/or suggest one or more color treatments to beapplied to the set of DAs to be displayed as part of a multimediapresentation.

As will be described in further detail herein, in some cases, thedetermination of a color treatment may be based, at least in part, on anaudio characteristic metadata item of a respective associated audiomedia item. As a simple example, if a “high energy” song (e.g., in termsof tempo, genre, beats per minute, average loudness, dynamic range,etc.) is selected as a first audio media item to serve as a soundtrackfor a given multimedia presentation of a set of DAs, then a warmerand/or higher contrast type of color treatment may be determined and/orsuggested for the multimedia presentation. Conversely, if a “low energy”song (e.g., in terms of tempo, genre, beats per minute, averageloudness, dynamic range, etc.) is selected as a first audio media itemto serve as a soundtrack for the given multimedia presentation of a setof DAs, then a cooler and/or lower contrast type of color treatment maybe determined and/or suggested for the multimedia presentation.

It is to be understood that “energy level” is just one type of audiocharacteristic that may be considered in the determination and/orsuggestion of one or more appropriate color treatments for a givenmultimedia presentation. Other audio characteristics (which may, e.g.,be stored in the form of audio characteristic metadata items associatedwith the audio media items) may include: an audio media item tempocharacteristic; an audio media item genre characteristic; an audio mediaitem mood characteristic; an audio media item artist characteristic; oran audio media item duration characteristic, etc. Likewise, colortemperature and contrast are just two exemplary characteristics by whicha color treatment may characterized. Other characteristics of colortreatments (e.g., from a set of a plurality of predetermined colortreatments) may include: a brightness characteristic; or a chromaticcharacteristic (e.g., how saturated the color treatment is, howmonochromatic the color treatment is, how wide of a color gamut thecolor treatment utilizes, etc.).

Returning now to FIG. 2 , an exemplary first determined color treatment210A for a first version of an exemplary multimedia presentation (i.e.,Memory 1A) is shown at 202A. Memory 1A 202A may comprise a caption 204(e.g., “Beach Day”), a featured cover image 205, and a play button 206.Memory 1A 202A also has an associated audio media item 208A, withassociated album artwork and metadata (e.g., an album cover art graphic,track name, artist name, album name, release year, genre, etc.). In thiscase, audio media item 208A comprises an exemplary “high energy” trackfrom one of a user's favorite artists. (It is to be understood thatvarious factors, the discussion of which is outside the scope of thisapplication, may be considered by the DAM when selecting an appropriateaudio media item(s) to suggest as a soundtrack for a particularmultimedia presentation.) Then, based, at least in part, on one or moreaudio characteristics of audio media item 208A, the DAM may determineand/or suggest that first determined color treatment 210A is anappropriate color treatment to apply to the set of DAs to be displayedas part of the multimedia presentation along with a soundtrack of audiomedia item 208A. In this case, because audio media item 208A comprisesan exemplary “high energy” track, first determined color treatment 210Amay comprise a relatively warm color temperature and low contrast colortreatment, i.e., assuming that “warm color temperature” and “lowcontrast” are color treatment characteristics that have been empirically(or otherwise) determined to complement high energy audio tracks well inthe context of a multimedia presentation, as will be explained infurther detail below with reference to FIG. 3 .

Moving to the right in FIG. 2 (i.e., following arrow 2121), e.g., inresponse to receiving input from a user of the device displaying theMemories interface 200 indicating a desire to preview other suggestedversions of Memory 202, an exemplary second determined color treatment210B for a second version of the Memory (i.e., Memory 1B) is shown at202B. Memory 1B 202B also has an associated audio media item 208B. Inthis case, audio media item 208B comprises an exemplary “mid-tempo”track from a popular artist (e.g., an artist popular in the user's areaof the world and/or an artist popular at the place and time where theDAs in the Memory 202 were originally captured, etc.). Again, based, atleast in part, on one or more audio characteristics of audio media item208B, the DAM may determine and/or suggest that second determined colortreatment 210B is an appropriate color treatment to apply to the set ofDAs to be displayed as part of the multimedia presentation along with asoundtrack of audio media item 208B. In this case, because audio mediaitem 208B comprises an exemplary “mid-tempo” track, second determinedcolor treatment 210B may comprise a relatively neutral color temperatureand low contrast color treatment, i.e., assuming that “neutral colortemperature” and “low contrast” are color treatment characteristics thathave been empirically (or otherwise) determined to complement mid-tempoaudio tracks well in the context of a multimedia presentation.

Finally, moving to the right again in FIG. 2 (i.e., following arrow2122), an exemplary third determined color treatment 210C for a thirdversion of the Memory (i.e., Memory 1C) is shown at 202C. Memory 1C 202Calso has an associated audio media item 208C. In this case, audio mediaitem 208C comprises an exemplary “chill” track (e.g., a low energy orrelaxing song) from a curated artist (e.g., an artist whose song hasbeen hand curated by musical experts as appropriate to accompany certainmultimedia presentations). Again, based, at least in part, on one ormore audio characteristics of audio media item 208C, the DAM maydetermine and/or suggest that third determined color treatment 210C isan appropriate color treatment to apply to the set of DAs to bedisplayed as part of the multimedia presentation along with a soundtrackof audio media item 208C. In this case, because audio media item 208Ccomprises an exemplary “chill” track, third determined color treatment210C may comprise a relatively cooler color temperature and low contrastcolor treatment, i.e., assuming that “cool color temperature” and “lowcontrast” are color treatment characteristics that have been empirically(or otherwise) determined to complement chill audio tracks well in thecontext of a multimedia presentation.

It is to be understood that, in other implementations, users may alsohave the option to manually select a desired audio media item to serveas the soundtrack for a multimedia presentation, at which point the DAMmay again re-determine and suggest an appropriate color treatment(s)based on the audio media item manually selected by the user. Likewise,in some implementations, a user may also have the option to manuallyselect a color treatment from a plurality of predetermined colortreatments (and/or create or modify their own personal color treatmentsfor application to DAs in multimedia presentations). Likewise, in stillother implementations, a user may also have the option to manuallyselect which DAs (or which portions of which DAs) are to be included ina multimedia presentations. In so doing, the overall duration of themultimedia presentation may be adjusted. Thus, the duration (and/orportion) of the selected audio media item serving as soundtrack for themultimedia presentation may also be adjusted, if desired, based on theduration and composition of the DAs in the multimedia presentation.

Turning now to FIG. 3 , a color treatment determination process 300based on a characteristic of a selected audio media item for amultimedia presentation of a set of DAs is illustrated, according to oneor more embodiments. Process 300 illustrates further detail regardingthe association of one or more audio characteristics of exemplary audiomedia item 208B from FIG. 2 with second determined color treatment 210B.

In the example of FIG. 3 , the possible color treatments that may bedetermined or suggested by the DAM comprises a plurality of ninepredetermined color treatment options. It is to be appreciated that more(or fewer) color treatment options may be possible. Further, in theexample of FIG. 3 , the plurality of predetermined color treatments maybe categorized by two color treatment characteristics into a 2D matrixof color treatment options. First, along horizontal axis 305, thepredetermined color treatments may be categorized by color temperature,e.g., with color treatments applying warmer color temperatures to DAs inthe multimedia presentation shown in Column A (illustrated with lightershading over the cover image), color treatments applying neutral colortemperatures shown in Column B (illustrated with medium darkness shadingover the cover image), and color treatments applying cooler colortemperatures shown in Column C (illustrated with darker shading over thecover image). Likewise, along vertical axis 310, the predetermined colortreatments may be categorized by contrast, e.g., with brighter colortreatments shown in Row 1 (illustrated with thinner lines in the coverimage), lower contrast color treatments shown in Row 2 (illustrated withmedium thickness lines in the cover image), and higher contrast colortreatments shown in Row 3 (illustrated with thicker lines in the coverimage).

Thus, as described above and illustrated in FIG. 3 , the color treatmentcharacteristics of color treatment 210B (located at “entry” B2 in the 2Dmatrix of color treatment options illustrated in FIG. 3 ) have beenempirically (or otherwise) determined to complement mid-tempo audiotracks, such as exemplary audio media item 208B, well in the context ofa multimedia presentation. It is to be appreciated that more (or fewer)color treatment characteristics may be considered when determining acolor treatment to suggest based on matching one or more audiocharacteristics of a selected audio media item that is to be used as asoundtrack to a multimedia presentation, thereby leading to higherdimensionality color treatment “matrices” (e.g., 3D, 4D, etc.) beingevaluated to determine a suggested color treatment for a given selectedaudio media item.

In some embodiments, matching the one or more audio characteristics of aselected audio media item to one or more characteristics of theplurality of predetermined color treatments may further comprise:comparing a first audio characteristic metadata item of the selectedaudio media item to a corresponding one or more characteristics of theplurality of predetermined color treatments, wherein, from among theplurality of predetermined color treatments, the first audiocharacteristic metadata item has a highest similarity to thecorresponding one or more characteristics of the color treatment that isdetermined to be the matching (or otherwise suggested) color treatmentfor the corresponding multimedia presentation.

In some cases, as described above with reference to FIG. 2 , a secondaudio media item associated with the set of DAs may be obtained, whereinthe second audio media item comprises at least a second audiocharacteristic metadata item (e.g., different from a first audiocharacteristic metadata item of a first audio media item), and a secondcolor treatment is determined based, at least in part, on the secondaudio characteristic metadata item of the second audio media item. Then,the determined second color treatment (which may also be different fromthe first color treatment) may be applied to at least one of the digitalassets of the first set of digital assets. This is also illustrated inFIG. 2 , with the suggestion of color treatment 210B to go along withthe version of the Memory 1B (202B), which is scored with audio mediaitem 208B; whereas, color treatment 210C is suggested to go along withthe version of the Memory 1C (202C), which is scored with audio mediaitem 208C.

In some embodiments, applying a determined first color treatment to atleast one of the digital assets of a first set of digital assets mayfurther comprise applying one or more predetermined tone curves, colorshifts, and/or monochromatic mappings corresponding to the determinedfirst color treatment to the at least one of the digital assets of thefirst set of digital assets, in order to achieve the desired look andfeel of the determined first color treatment for the multimediapresentation. The first color treatment may be embodied, e.g., as athree-dimensional look-up table (LUT), taking {R, G, B} color tripletsfor the input pixel values and converting them into {R, G, B} colortriplets for the corresponding output (i.e., color-treated) pixelvalues.

Exemplary Digital Asset Layouts

Turning now to FIG. 4A, examples 400 of possible digital asset layoutsfor the DAs to be displayed in a multimedia presentation areillustrated, according to one or more embodiments. In FIG. 4A, there isan exemplary set of digital assets 402 that comprises: a first landscapeorientation image having a single subject (402 ₁), a first portraitorientation image having a nature scene of a tree (402 ₂), and a secondlandscape orientation image having multiple subjects (402 ₃). It is tobe understood that a given set of digital assets may have many moreimages, as well as moving images, videos of various durations, andimages of varying sizes, resolutions, dimensions and/or orientations.

According to some embodiments, multimedia presentations may comprise asequence of DA layouts that are displayed on a display device (e.g., inconjunction with the playback of one or more audio media items, asdescribed above), wherein each DA layout may comprise predeterminedpreferred clusters of one or more DAs, which are laid out in a desiredpattern or arrangement, e.g., a “single image” layout (such as Layout A404A, including only image 402 ₁), a “diptych” layout with two images(such as Layout B 404B, including images 402 ₁ and 402 ₂), a “triptych”layout with three images (such as Layout C 404C, including images 402 ₁,402 ₂, and 402 ₃). In some cases, DA layouts may be preferred that areorientation agnostic, i.e., they provide a satisfactory viewingexperience, no matter what orientation the display device is in duringplayback of the multimedia presentation.

Each DA in a given DA layout may have a preferred duration during themultimedia presentation (e.g., 0.5 seconds on screen, 1 second onscreen, etc.). In the case of a DA that is a video file, someembodiments may also intelligently determine edits to the durationand/or content of individual video file, such that the video file onlytakes up its allotted duration in the multimedia presentation (e.g., 5seconds), and so that a visually interesting portion of the video file(e.g., portions of the video including one or more high quality faces, aperson of interest, an object(s) of interest, particular lighting orbackground scenery, color composition, or motion characteristics) isclipped and used for the allotted duration in the multimediapresentation. Each DA in a given DA layout may also have a preferredtransition type to lead to the next DA that is to be displayed duringthe multimedia presentation, e.g., slide left (as illustrated by arrow408), slide right (as illustrated by arrows 406 or 4082), slide up (asillustrated by arrow 4102), slide down (as illustrated by arrows 410 ₁or 410 ₃), fade through white, fade through black, dissolve, cut, etc.Each DA in a given DA layout may also have a special treatment or effectapplied to it, if desired (e.g., a rotation, zoom, pan, etc.). In somecases, the special treatment may override a default effect beingotherwise applied to a DA based on the specified parameters for a givenmultimedia presentation.

In some cases, one or more DAs may be excluded from particular DAlayouts, and/or one or more DA layouts may be excluded from a givenmultimedia presentation, to avoid situations where a given DA may beforced to be displayed in a visually unpleasing way during themultimedia presentation. For example, as shown in FIG. 4A, image 402 ₃has been excluded from consideration for inclusion in the right panel ofa DA layout of type Layout C 404C, due to the fact that the dimensionsand aspect ratio of the right panel of Layout C 404C are not conduciveto a crop of image 402 ₃ that would leave both human subjects visible inthe crop. Another option would be to leave image 402 ₃ uncropped butmoved down to the bottom edge of the right panel of Layout C 404C, butthis may, in turn, leave unwanted “dead space” above image 402 ₃ in theDA layout. Assuming similar problems would be faced when attempting tocrop image 402 ₃ to fit in each of the other panels of Layout C 404C, aswell, it may simply be deemed by the DAM that image 402 ₃ should beexcluded from consideration for inclusion in a DA layout of type LayoutC 404C. By contrast, because image 402 ₁ has only a single humansubject, as indicated by arrows 403/405/407, it may be possible to findacceptable crops of image 402 ₁ that fit into each of: the single-panelLayout A 404A; the left panel of Layout B 404B; and the left panel ofLayout C 404C. Thus, it may be deemed by the DAM that image 402 ₁ wouldbe permitted to participate in any of the Layouts 404A, 404B, or 404Cduring a multimedia presentation.

It is to be understood that these examples are presented merely forillustrative purposes, and that many other factors or considerations maybe evaluated when determining: which DAs should be clustered together(e.g., for use together in a single DA layout); which DA layouts areappropriate to use; which DAs may be used in which panels of which DAlayouts; how long each DA layout should be displayed during themultimedia presentation; which portions of the content of each DA shouldbe displayed during the multimedia presentation; and how/when each DA(or set of DAs) should be transitioned to the display of the next DA (orset of DAs) that are to be displayed in the multimedia presentation,etc.

Exemplary Digital Asset Transitions

Turning now to FIG. 4B, examples 450 of possible digital asset clusters,transition types, and transition durations for the DAs to be displayedin a multimedia presentation are illustrated, according to one or moreembodiments. In FIG. 4B, an exemplary set of DAs 450 comprises tenindividual DAs, in this case, images 452 ₁-452 ₁₀. As illustrated, DAs452 have been grouped into three clusters 458 (i.e., images 452 ₁-452 ₃in a first cluster, images 452 ₄-452 ₇ in a second cluster, and images452 ₈-452 ₁₀ in a third cluster). DAs may be placed into clusters forany number of reasons, e.g., based on having related content, comingfrom a related moment in a user's metadata network, all having an imageof a particular person, location, or scene, or even based on theirorientations and/or dimensions working well together for a particular DAlayout, and so forth. Each cluster of DAs may have one or more preferredeffects applied to them during the multimedia presentation, e.g., apanning effect 454 ₁ being applied to images 452 ₁-452 ₃ in the firstcluster, a scaling effect 454 ₂ being applied to images 452 ₄-452 ₇ inthe second cluster, and another (possibly different) panning effect 454₃ being applied to images 452 ₈-452 ₁₀ in the third cluster. Each DA (orcluster of DAs) may also have a preferred duration 462 for which theyare displayed during the multimedia presentation. Further, as mentionedabove, each DA may also have a special treatment or effect 460 appliedto it, if desired (e.g., a rotation, zoom, pan, etc.). In some cases,the special treatment may override a default effect being otherwiseapplied to a DA based on the specified parameters for a given multimediapresentation.

In some cases, one or more special transitions (e.g., as shown at 4561and 4562) may also be applied between particular DAs in the multimediapresentation. As one example, when the topics or content of the imagesin a multimedia presentation make a significant change (e.g., if images452 ₁-452 ₃ from the first cluster were captured in 2018, images 452₄-452 ₇ from the second cluster were captured in 2019, and images 452₈-452 ₁₀ from the third cluster were captured in 2020), then a moredramatic (or longer lasting) transition (e.g., a fade to black, fadethrough white, etc.) could be applied between those clusters of DAs thanwould be preferred or selected for application between other clusters ofDAs that perhaps did not exhibit as significant of changes between oneanother.

Thus, as may now be understood, the preferred DA and/or transitiondurations, preferred transition types, preferred DA layouts, and/orpreferred sequencing of DAs (or, indeed, the included DAs themselvesand/or the particular portions thereof) may be determined for themultimedia presentation based, at least in part, on the one or moreaudio characteristic metadata items of the selected audio media item forthe multimedia presentation. For example, a larger number of more rapidDA transitions may be more appropriate to accompany a higher energyaudio media item soundtrack for a multimedia presentation, whereas asmaller number of more gradual DA transitions may be more appropriate toaccompany a lower energy or chill audio media item soundtrack for amultimedia presentation. Further, in some implementations, the preferredtiming and/or style of transitions between DAs in a multimediapresentation may be based, at least in part, on the content of theselected audio media item soundtrack for the multimedia presentation.For example, it may be desirable to synchronize the transitions betweencertain DAs with the beat of the audio media item and/or save specialtransitions and/or special treatments of DAs for the most important orimpactful moments of music within an audio media item. As anotherexample, the duration and/or composition (i.e., which particularportion) of the audio media item may dynamically change based on theduration and/or content of the multimedia presentation.

It is also to be understood that, in some embodiments, the preferred DAand/or transition durations, preferred transition types, preferred DAlayouts, and/or preferred sequencing of DAs (or, indeed, the includedDAs themselves) may also be altered on-the-fly during playback 464 ofthe multimedia presentation by a user (e.g., by a user holding down afinger or mouse click on a particular DA to “pause” the preferredpresentation sequence of DAs until the user lifts their finger orreleases the mouse click—even while the audio soundtrack for themultimedia presentation may continue to play while the display sequenceof the DAs is frozen), hence their designation as “preferred”characteristics at certain places herein. In some embodiments, thedetermined one or more parameters for the multimedia presentation may beexported by the DAM in the form of a so-called presentation decisionlist 466 to a multimedia presentation playback engine, which multimediapresentation playback engine is configured to implement and render thevarious preferences expressed in the presentation decision list, unlessor until one or more preferences are overridden by a user (or becomeimpossible for the playback engine to implement for some other reason,such as device capabilities or orientation). Characteristics of themultimedia presentation specified in the presentation decision list 466may comprise one or more of: the selected color treatment for themultimedia presentation, the selected audio media item (including,optionally, an indication of a volume level that the audio media itemshould be mixed in to the multimedia presentation at, e.g., asforeground music, background music, attenuated when a video DA isplaying back its own audio during the presentation, etc.), the preferredDA sequence, preferred DA clusters, preferred DA layouts, preferredtransition types, or preferred transition durations, as well as whichDAs (or portions thereof) to even include in the multimedia presentation(e.g., in the event that a user wishes to remove or add a specific DA,or portion thereof, from the multimedia presentation).

Exemplary Deep Neural Network Architecture for Performing ColorNormalization Operations

Turning now to FIG. 5 , an exemplary deep neural network (DNN)architecture 500 for a network trained to learn image parametermodifications to apply to an image, in order to approximate a targetstyle is illustrated, according to one or more embodiments. Inputtraining image 502 represents an exemplary unmodified image that may beused during the training process for the exemplary DNN illustrated inFIG. 5 . Target image 518 represents a version of training image 502that has been modified to have a first target style (e.g., a colorneutral style, a low contrast style, a style that is reflective of aparticular professional photographer, etc.). As will be understood, byusing a DNN, such as is shown in DNN architecture 500, to apply asimilar target style to each DA in a set of DAs that is to be displayedin multimedia presentation, the color differences between the individualDAs in the set of DAs (e.g., differences due to the DAs within a set ofDAs being captured at different times, by different cameras, underdifferent lighting conditions, subjected to differing post-processingcolor treatments, etc.) may be normalized. Color normalizing a set ofDAs in a similar fashion to one another prior to the application of acommon color treatment can lead to a more consistent set of results anda more cohesive and/or visually-pleasing look and feel to a generatedmultimedia presentation including the color treated DAs.

According to some embodiments, input training image 502 may first bedownsampled (504) to a desired smaller resolution, e.g., in order tosave processing/thermal resources and/or to simplify and speed updownstream processing by the DNN(s). In some architectures, the inputimage may first be applied to all or a portion of a pre-trained sceneclassification neural network (NN) 506, which may be trained to identifyor classify certain aspects of the scenes represented in input trainingimages, such as input training image 502, and which may provideinformation, e.g., in the form of a matrix of values, representative ofthe identified scene, which may be leveraged by other parts of the DNNarchitecture to speed up the training process and/or allow the trainingprocess to work with less training data.

Next, the information output from scene classification NN 506 may beinput to the DNN 508 that is being trained in the example illustrated inFIG. 5 . In particular, DNN 508 may be trained to determine one or moreparameter estimation heads 512, such as Param_1 through Param_6 shown inFIG. 5 , wherein, e.g., one image parameter modification may beestimated for each of image parameter of a set of image parameters thatthe DNN 508 is attempting to learn how to modify, in order to causeinput images to approximate the target image style when the DNN isapplied to images at inference time. In some cases, the DNN 508 may befurther configured to determine an attention map 510 (i.e., a 2D imagemap of values indicating the regions of the input image where themodifications related to a respective image parameter are applicable,e.g., a parameter estimation head related to a black point imageparameter may focus on the very dark regions within an input image sincethey are the most relevant to black point modifications, and so forth)for one or more of the parameters in the set of image parameters, whichmay be used to aid in the training process. In some cases, the set ofimage parameters for which image parameter modifications are determinedby the DNN may comprise one or more of: a white balance parameter; alocal light parameter; an exposure parameter; a black point parameter; ahighlight parameter; a contrast parameter; a vibrancy parameter; or acolor tint parameter. As will be understood, any desired set of imageparameters may be used in a given implementation.

During training, one or more training modules 522 may be employed by thenetwork. For example, an image parameter loss term 514 may be determinedfor one or more of the learned image parameters in the set of imageparameters. In some cases, one or more image parameter loss terms may becombined to form a combined image parameter loss term. During each roundof training, a predicted edit 516 image version of the input trainingimage 502 may be generated, i.e., by applying the one or more of theestimated image parameter modifications for the image parameters thatthe DNN 508 is attempting to learn to the input training image 502. Atraining image loss function value 520 may then be computed for theinput training image 502, based on a calculated difference between thepredicted edit image version 516 and the corresponding “ground truth”target image 518, i.e., the version of the input image that has beenmanually modified to have the desired target image style. The lossfunction output may then be backpropagated all the way through the DNNto update the weights, and the training process may continue until theDNN converges to an acceptable loss function value.

As may now be appreciated, at block 524, the trained DNN 508 has learnedhow to make image parameter modifications to input images at inferencetime in order to modify the input images to achieve the target imagestyle. Assuming, as in this example, that the target image style is acolor neutral image style, then applying the DNN architecture 500 to DAsin a set of DAs that are to be displayed in multimedia presentation cancolor normalize the DAs, effectively bringing them into a commonconnection “color space” before any further color treatment operationsare to be applied to the DAs, such that any further color treatmentoperations result in a consistent color look and feel across the DAs inthe set of DAs.

In some embodiments, the determined set of image parameter modificationsfor a given input image may be stored in a memory separately from theimage. In other embodiments, the determined set of image parametermodifications for a given input image may be stored in the metadata ofthe image. These storage options may allow for the image modificationsdetermined by the DNN to be applied at a later time, applied on-demand(e.g., only when a image is being displayed as a part of a color treatedmultimedia presentation), and/or removed from the image (e.g., ifinitially applied by a user who later prefers to apply their own imagecolor modifications to an image). In some embodiments, the imagemodifications determined by the DNN may be applied in a singleoperation, i.e., along with any other color treatment operations beingapplied to the image, e.g., in the form of a single three-dimensionalLUT, configured to perform both color normalization and color treatmentoperations.

In some embodiments, other types of loss functions may be applied atblock 520 (or other places throughout the DNN). For example, the DNN maybe further trained using a loss function based on determining adifference in chromaticity of skin tone pixels in an input trainingimage and a canonical skin tone chromaticity point(s). In otherexamples, the DNN may be further trained using a loss function based ondetermining a difference in the amount of green (or magenta) tint inpixels in the input training image and canonical amounts of green (ormagenta) tint, respectively, expected in pixels of typical (e.g.,properly color-corrected) captured images.

As may be understood, at inference time, each of a first DA in a set ofDAs, as well as a second one or more DAs from the set of DAs may beapplied to the DNN, either in sequential order or parallel, wherein theDNN is configured to determine and apply a set of image parametermodifications for the set of image parameters to each DA in the set ofDAs. As described above, once each DA in the set of DAs has been colornormalized based on application of the outputs of the DNN, a determinedcolor treatment may sagely be applied to each DA in the set of DAs.

Exemplary Methods for Performing Digital Asset Color Treatments, ColorNormalization, and Determining Parameters for Multimedia Presentationsof Sets of Digital Assets

Turning now to FIG. 6A, a flow chart is shown, illustrating a method 600of determining a color treatment for a set of DAs based on acharacteristic of an associated audio media item, according to variousembodiments. First, at Step 602 the method 600 may obtain a first set ofdigital assets. Next, at Step 604 the method 600 may obtain a firstaudio media item associated with the first set of digital assets,wherein the first audio media item comprises at least a first audiocharacteristic metadata item. In some cases, the audio characteristicmetadata may comprise one or more of: tempo, genre, energy, mood,artist, duration (Step 605). Next, at Step 606 the method 600 maydetermine a first color treatment, wherein the determination of thefirst color treatment is based, at least in part, on the first audiocharacteristic metadata item of the first audio media item. In somecases, the determination of the first color treatment may furthercomprise comparing the first audio characteristic metadata item to acorresponding one or more characteristics of a plurality ofpredetermined color treatments (Step 607). Next, at Step 608 the method600 may apply the determined first color treatment to at least one ofthe digital assets of the first set of digital assets. In some cases, asdescribed below with reference to FIG. 6C, a color normalizationoperation may be applied to the first set of digital assets prior to theapplication of the determined first color treatment. Finally, at Step610 the method 600 may determine one or more parameters for a multimediapresentation of the first set of digital assets based, at least in part,on the first audio characteristic metadata item of the first audio mediaitem. In some cases, the parameters for the multimedia presentation maycomprise one or more of: the determined first color treatment; the firstaudio media item; a preferred DA sequence; a preferred set of DAclusters; a preferred set of DA layouts; a determination of which DAs(and portions thereof) to include in the multimedia presentation; apreferred set of transition types; or a preferred set of transitiondurations (Step 611).

Turning now to FIG. 6B, a flow chart is shown, illustrating a method 620of determining one or more parameters for a multimedia presentation of aset of DAs based on a characteristic of an associated audio media item,according to various embodiments. First, at Step 622 the method 600 mayobtain a first set of digital assets. Next, at Step 624 the method 620may obtain a first audio media item associated with the first set ofdigital assets, wherein the first audio media item comprises at least afirst audio characteristic metadata item. In some cases, the audiocharacteristic metadata may comprise one or more of: tempo, genre,energy, mood, artist, duration (Step 625). Finally, at Step 626 themethod 620 may determine one or more parameters for a multimediapresentation of the first set of digital assets based, at least in part,on the first audio characteristic metadata item of the first audio mediaitem. In some cases, the parameters for the multimedia presentation maycomprise one or more of: a preferred DA sequence; a preferred set of DAclusters; a preferred set of DA layouts; a determination of which DAs(and portions thereof) to include in the multimedia presentation; apreferred set of transition types; or a preferred set of transitiondurations (Step 627).

Turning now to FIG. 6C, a flow chart is shown, illustrating a method 640of using a DNN trained to learn image parameter modifications to applyto an image to approximate a target style, according to variousembodiments. First, at Step 642 the method 640 may obtain a firstdigital image. Next, at Step 644 the method 640 may optionally downscalethe first digital image, e.g., to improve the performance and/or lessenthe amount of processing resources required to apply a DNN to the firstdigital image data. Next, at Step 646 the method 640 may apply the(optionally downscaled) first digital image to a first Deep NeuralNetwork (DNN). In some cases, the first DNN may have been trained tolearn a first target image style (Step 648). In other cases, the firstDNN may be configured to determine a first set of image parametermodifications for a first set of image parameters of the first digitalimage (e.g., white balance, local light, exposure, black point,highlight, contrast, vibrancy, tint) (Step 650). In other cases, theapplication of the determined first set of image parameter modificationsto the first digital image cause the first digital image to approximatethe first target style (Step 652).

Next, at Step 654 the method 640 may apply the determined first set ofimage parameter modifications to the first digital image, i.e., in orderto color normalize the content of the first digital image. In somecases, the determined first set of image parameter modifications mayalso optionally be stored in a memory (e.g., in metadata of firstdigital image or separately) (Step 656), such that the modificationscould be applied, removed, and/or re-applied at a later time in on ormore editing applications. In some cases, once the first digital imageis color normalized, the method 640 may optionally apply a determinedfirst color treatment to the first digital image (Step 658), e.g., asdescribed above with reference to FIG. 6A.

It is to be understood that the operations of method 640 may likewise beapplied to a single digital image or to multiple digital images, e.g.,to each DA in a set of DAs to be included in a multimedia presentation,either serially or in parallel, so that the color properties of all DAsin the set of DAs are normalized with one another before the applicationof a common determined color treatment to the set of DAs.Color-normalizing a set of DAs in a similar fashion prior to theapplication of a common color treatment can lead to a more consistentset of results and a more cohesive and/or visually-pleasing look andfeel to a generated multimedia presentation including the color treatedDAs.

Exemplary Electronic Computing Devices

Referring now to FIG. 7 , a simplified functional block diagram ofillustrative programmable electronic computing device 700 is shownaccording to one embodiment. Electronic device 700 could be, forexample, a mobile telephone, personal media device, portable camera, ora tablet, notebook or desktop computer system. As shown, electronicdevice 700 may include processor 705, display 710, user interface 715,graphics hardware 720, device sensors 725 (e.g., proximitysensor/ambient light sensor, accelerometer, inertial measurement unit,and/or gyroscope), microphone 730, audio codec(s) 735, speaker(s) 740,communications circuitry 745, image capture device 750, which may, e.g.,comprise multiple camera units/optical image sensors having differentcharacteristics or abilities (e.g., Still Image Stabilization (SIS),HDR, OIS systems, optical zoom, digital zoom, etc.), video codec(s) 755,memory 760, storage 765, and communications bus 770.

Processor 705 may execute instructions necessary to carry out or controlthe operation of many functions performed by electronic device 700(e.g., such as the generation and/or processing of images in accordancewith the various embodiments described herein). Processor 705 may, forinstance, drive display 710 and receive user input from user interface715. User interface 715 can take a variety of forms, such as a button,keypad, dial, a click wheel, keyboard, display screen and/or a touchscreen. User interface 715 could, for example, be the conduit throughwhich a user may view a captured video stream and/or indicate particularimage frame(s) that the user would like to capture (e.g., by clicking ona physical or virtual button at the moment the desired image frame isbeing displayed on the device's display screen). In one embodiment,display 710 may display a video stream as it is captured while processor705 and/or graphics hardware 720 and/or image capture circuitrycontemporaneously generate and store the video stream in memory 760and/or storage 765. Processor 705 may be a system-on-chip (SOC) such asthose found in mobile devices and include one or more dedicated graphicsprocessing units (GPUs). Processor 705 may be based on reducedinstruction-set computer (RISC) or complex instruction-set computer(CISC) architectures or any other suitable architecture and may includeone or more processing cores. Graphics hardware 720 may be specialpurpose computational hardware for processing graphics and/or assistingprocessor 705 perform computational tasks. In one embodiment, graphicshardware 720 may include one or more programmable graphics processingunits (GPUs) and/or one or more specialized SOCs, e.g., an SOC speciallydesigned to implement neural network and machine learning operations(e.g., convolutions) in a more energy-efficient manner than either themain device central processing unit (CPU) or a typical GPU, such asApple's Neural Engine processing cores.

Image capture device 750 may comprise one or more camera unitsconfigured to capture images, e.g., images which may be processed togenerate color-treated versions of said captured images, e.g., inaccordance with this disclosure. Output from image capture device 750may be processed, at least in part, by video codec(s) 755 and/orprocessor 705 and/or graphics hardware 720, and/or a dedicated imageprocessing unit or image signal processor incorporated within imagecapture device 750. Images so captured may be stored in memory 760and/or storage 765. Memory 760 may include one or more different typesof media used by processor 705, graphics hardware 720, and image capturedevice 750 to perform device functions. For example, memory 760 mayinclude memory cache, read-only memory (ROM), and/or random accessmemory (RAM). Storage 765 may store media (e.g., audio, image and videofiles), computer program instructions or software, preferenceinformation, device profile information, and any other suitable data.Storage 765 may include one more non-transitory storage mediumsincluding, for example, magnetic disks (fixed, floppy, and removable)and tape, optical media such as CD-ROMs and digital video disks (DVDs),and semiconductor memory devices such as Electrically ProgrammableRead-Only Memory (EPROM), and Electrically Erasable ProgrammableRead-Only Memory (EEPROM).

Memory 760 and storage 765 may be used to retain computer programinstructions or code organized into one or more modules and written inany desired computer programming language. When executed by, forexample, processor 705, such computer program code may implement one ormore of the methods or processes described herein. Power source 775 maycomprise a rechargeable battery (e.g., a lithium-ion battery, or thelike) or other electrical connection to a power supply, e.g., to a mainspower source, that is used to manage and/or provide electrical power tothe electronic components and associated circuitry of electronic device700.

As described above, one aspect of the present technology is thegathering and use of data available from various sources to improve thedelivery to users of content-related suggestions. The present disclosurecontemplates, that in some instances, this gathered data may includepersonal information data that uniquely identifies or can be used tocontact or locate a specific person. Such personal information data caninclude demographic data, location-based data, telephone numbers, emailaddresses, social media handles, home addresses, data or recordsrelating to a user's health or level of fitness (e.g., vital signsmeasurements, medication information, exercise information), date ofbirth, or any other identifying or personal information.

The present disclosure recognizes that the use of such personalinformation data, in the present technology, can be used to the benefitof users. For example, the personal information data can be used todeliver targeted content-related suggestions that are of greaterinterest and/or greater contextual relevance to the user. Accordingly,use of such personal information data enables users to have morestreamlined and meaningful control of the content that they view and/orshare with others. Further, other uses for personal information datathat benefit the user are also contemplated by the present disclosure.For instance, health and fitness data may be used to provide insightsinto a user's general wellness, or state of well-being during variousmoments or events in their lives.

The present disclosure contemplates that the entities responsible forthe collection, analysis, disclosure, transfer, storage, or other use ofsuch personal information data will comply with well-established privacypolicies and/or privacy practices. In particular, such entities shouldimplement and consistently use privacy policies and practices that aregenerally recognized as meeting or exceeding industry or governmentalrequirements for maintaining personal information data private andsecure. Such policies should be easily accessible by users and should beupdated as the collection and/or use of data changes. Personalinformation from users should be collected for legitimate and reasonableuses of the entity and not shared or sold outside of those legitimateuses. Further, such collection/sharing should occur after receiving theinformed consent of the users. Additionally, such entities shouldconsider taking any needed steps for safeguarding and securing access tosuch personal information data and ensuring that others with access tothe personal information data adhere to their privacy policies andprocedures. Further, such entities can subject themselves to evaluationby third parties to certify their adherence to widely accepted privacypolicies and practices. In addition, policies and practices should beadapted for the particular types of personal information data beingcollected and/or accessed and adapted to applicable laws and standards,including jurisdiction-specific considerations. For instance, in the US,collection of or access to certain health data may be governed byfederal and/or state laws, such as the Health Insurance Portability andAccountability Act (HIPAA); whereas health data in other countries maybe subject to other regulations and policies and should be handledaccordingly. Hence, different privacy practices should be maintained fordifferent personal data types in each country.

Despite the foregoing, the present disclosure also contemplatesembodiments in which users selectively block the use of, or access to,personal information data. That is, the present disclosure contemplatesthat hardware and/or software elements can be provided to prevent orblock access to such personal information data. For example, in the caseof content-related suggestion services, the present technology can beconfigured to allow users to select to “opt in” or “opt out” ofparticipation in the collection of personal information data duringregistration for services or anytime thereafter. In another example,users can select not to provide their content and other personalinformation data for improved content-related suggestion services. Inyet another example, users can select to limit the length of time theirpersonal information data is maintained by a third party, limit thelength of time into the past from which content-related suggestions maybe drawn, and/or entirely prohibit the development of a knowledge graphor other metadata profile. In addition to providing “opt in” and “optout” options, the present disclosure contemplates providingnotifications relating to the access or use of personal information. Forinstance, a user may be notified, upon downloading an “App,” that theirpersonal information data will be accessed and then reminded again justbefore personal information data is accessed by the App.

Moreover, it is the intent of the present disclosure that personalinformation data should be managed and handled in a way to minimizerisks of unintentional or unauthorized access or use. Risk can beminimized by limiting the collection of data and deleting data once itis no longer needed. In addition, and when applicable, such as withincertain health-related applications, data de-identification can be usedto protect a user's privacy. De-identification may be facilitated, whenappropriate, by removing specific identifiers (e.g., date of birth,etc.), controlling the amount or specificity of data stored (e.g.,collecting location data a city level rather than at an address level),controlling how data is stored (e.g., aggregating data across users),and/or other methods.

Therefore, although the present disclosure broadly covers use ofpersonal information data to implement one or more various disclosedembodiments, the present disclosure also contemplates that the variousembodiments can also be implemented without the need for accessing suchpersonal information data. That is, the various embodiments of thepresent technology are not rendered inoperable due to the lack of all ora portion of such personal information data. For example, content can besuggested for use by users by inferring preferences based onnon-personal information data or a bare minimum amount of personalinformation, such as the quality level of the content (e.g., focus,exposure levels, musical quality or suitability, etc.) or the fact thatcertain content is being requested by a device associated with a contactof the user, other non-personal information available to the DAM system,or publicly available information.

It is to be understood that the above description is intended to beillustrative, and not restrictive. For example, the above-describedembodiments may be used in combination with each other. Many otherembodiments will be apparent to those of skill in the art upon reviewingthe above description. The scope of the invention therefore should bedetermined with reference to the appended claims, along with the fullscope of equivalents to which such claims are entitled.

What is claimed is:
 1. A device, comprising: a memory; a display; andone or more processors operatively coupled to the memory, wherein theone or more processors are configured to execute instructions causingthe one or more processors to: obtain a first set of digital assets;obtain a first audio media item associated with the first set of digitalassets, wherein the first audio media item comprises at least a firstaudio characteristic metadata item; determine a first color treatment,wherein the determination of the first color treatment is based, atleast in part, on the first audio characteristic metadata item of thefirst audio media item; and apply the determined first color treatmentto at least one of the digital assets of the first set of digitalassets.
 2. The device of claim 1, wherein the one or more processors arefurther configured to execute instructions causing the one or moreprocessors to: determine one or more parameters for a multimediapresentation of the first set of digital assets based, at least in part,on the first audio characteristic metadata item of the first audio mediaitem.
 3. The device of claim 2, wherein the one or more parameters forthe multimedia presentation of the first set of digital assets compriseone or more of: an identification of the determined first colortreatment; an identification of the first audio media item; a preferredsequence for the digital assets of the first set of digital assets inthe multimedia presentation; a preferred set of digital asset clustersfor the digital assets of the first set of digital assets in themultimedia presentation; a preferred set of digital asset layouts forthe digital assets of the first set of digital assets in the multimediapresentation; a portion of a digital asset of the first set of digitalassets to include in the multimedia presentation; a preferred set oftransition types to be used for the digital assets of the first set ofdigital assets in the multimedia presentation; or a preferred set oftransition durations to be used for the digital assets of the first setof digital assets in the multimedia presentation.
 4. The device of claim1, wherein the first audio characteristic metadata item of the firstaudio media item comprises one or more of: an audio media item tempocharacteristic; an audio media item genre characteristic; an audio mediaitem energy characteristic; an audio media item mood characteristic; anaudio media item artist characteristic; or an audio media item durationcharacteristic.
 5. The device of claim 1, wherein the instructionscausing the one or more processors to apply the determined first colortreatment to at least one of the digital assets of the first set ofdigital assets further comprise instructions to: apply one or morepredetermined tone curves corresponding to the determined first colortreatment to the at least one of the digital assets of the first set ofdigital assets.
 6. The device of claim 1, wherein the instructionscausing the one or more processors to determine the first colortreatment based, at least in part, on the first audio characteristicmetadata item of the first audio media item further compriseinstructions to: compare the first audio characteristic metadata item ofthe first audio media item to a corresponding one or morecharacteristics of a plurality of predetermined color treatments,wherein the plurality of predetermined color treatments includes thefirst color treatment, and wherein, from among the plurality ofpredetermined color treatments, the first audio characteristic metadataitem has a highest similarity to the corresponding one or morecharacteristics of the first color treatment.
 7. The device of claim 6,wherein the one or more characteristics of the plurality ofpredetermined color treatments comprises one or more of: a brightnesscharacteristic; a contrast characteristic; a color temperaturecharacteristic; or a chromatic characteristic.
 8. The device of claim 1,wherein the one or more processors are further configured to executeinstructions causing the one or more processors to: determine apreferred set of transition types to be used for the digital assets ofthe first set of digital assets in the multimedia presentation, whereinthe determination of the preferred set of transition types is based, atleast in part, on the first audio characteristic metadata item.
 9. Thedevice of claim 1, wherein the one or more processors are furtherconfigured to execute instructions causing the one or more processorsto: determine a preferred set of transition durations to be used for thedigital assets of the first set of digital assets in the multimediapresentation, wherein the determination of the preferred set oftransition durations is based, at least in part, on the first audiocharacteristic metadata item.
 10. The device of claim 1, wherein the oneor more processors are further configured to execute instructionscausing the one or more processors to: obtain a second audio media itemassociated with the first set of digital assets, wherein the secondaudio media item comprises at least a second audio characteristicmetadata item; determine a second color treatment, wherein thedetermination of the second color treatment is based, at least in part,on the second audio characteristic metadata item of the second audiomedia item; and apply the determined second color treatment to at leastone of the digital assets of the first set of digital assets, whereinthe first color treatment is different from the second color treatment.11. An image processing method, comprising: obtaining a first digitalimage; applying the first digital image to a first deep neural network(DNN), wherein the first DNN has been trained to learn a first targetimage style, wherein the first DNN is configured to determine a firstset of image parameter modifications for a first set of image parametersof the first digital image, and wherein application of the determinedfirst set of image parameter modifications to the first digital imagewould cause the first digital image to approximate the first targetstyle; and applying the determined first set of image parametermodifications to the first digital image.
 12. The method of claim 11,wherein the first DNN has been further trained using a loss functionbased on determining a difference in chromaticity of skin tone pixels inthe first digital image and a canonical skin tone chromaticity point.13. The method of claim 11, wherein the first DNN has been furthertrained using a loss function based on determining a difference in greenor magenta tint in pixels in the first digital image and canonicalamounts of green or magenta tint, respectively, expected in pixels ofcaptured images.
 14. The method of claim 11, wherein the first DNN isfurther configured to determine an attention map for each of the firstset of image parameters.
 15. The method of claim 11, further comprising:obtaining a second one or more digital images, wherein the first digitalimage and the second one or more digital images comprise a first set ofdigital images; applying each of the second one or more digital imagesto the first DNN, wherein the first DNN is configured to determine asecond set of image parameter modifications for the first set of imageparameters for each of the second one or more digital images, andwherein application of each of the determined second sets of imageparameter modifications, respectively, to the second one or more digitalimages would cause each of the second one or more digital images to moreclosely approximate the first target style; and applying each of thedetermined second sets of image parameter modifications, respectively,to the second one or more digital images.
 16. An image processingmethod, comprising: obtaining a first set of digital assets; obtaining afirst audio media item associated with the first set of digital assets,wherein the first audio media item comprises at least a first audiocharacteristic metadata item; and determining one or more parameters fora multimedia presentation of the first set of digital assets based, atleast in part, on the first audio characteristic metadata item of thefirst audio media item.
 17. The method of claim 16, wherein the one ormore parameters for the multimedia presentation of the first set ofdigital assets comprise one or more of: a preferred set of digital assetlayouts for the digital assets of the first set of digital assets in themultimedia presentation; a portion of a digital asset of the first setof digital assets to include in the multimedia presentation; a preferredset of transition types to be used for the digital assets of the firstset of digital assets in the multimedia presentation; or a preferred setof transition durations to be used for the digital assets of the firstset of digital assets in the multimedia presentation.
 18. The method ofclaim 16, further comprising: determining a preferred set of digitalasset layouts for the digital assets of the first set of digital assetsin the multimedia presentation, wherein the determination of thepreferred set of digital asset layouts is based, at least in part, onthe first audio characteristic metadata item.
 19. The method of claim16, further comprising: determining a preferred set of transition typesto be used for the digital assets of the first set of digital assets inthe multimedia presentation, wherein the determination of the preferredset of transition types is based, at least in part, on the first audiocharacteristic metadata item.
 20. The method of claim 16, furthercomprising: determining a first color treatment, wherein thedetermination of the first color treatment is based, at least in part,on the first audio characteristic metadata item of the first audio mediaitem; and applying the determined first color treatment to at least oneof the digital assets of the first set of digital assets.